knowledge: learning kernel — model the estimator bias, not just sampling

Revisiting Layer 1 against Layer 1.5 (and Riis 2026, arXiv:2604.08554):
neutral Wright-Fisher is a null that BOTH neural architectures deviate
from, in opposite directions. Add a learning kernel to the refit step,
p_{t+1} = T_theta(counts/n), with two population-genetics knobs -- reset u
(mutation toward a prior = smoothing) and temperature tau (sharpening =
mode-competition) -- both identity by default, so the histogram bridge and
all 68 scientific-validation/correctness tests are unchanged.

Result: neutral drift fails both neural models, oppositely.
- VAE regime (n=6000, K=30): neutral drift is inert (no collapse), yet the
  real VAE collapsed to one mode. Sharpening tau=0.8 reproduces it -- the
  estimator ADDS collapse pressure.
- RNN regime (n=200, K=256): neutral drives H->0, but the real RNN only
  partially collapses. Mutation u=0.006 reproduces the H-floor -- the
  estimator REMOVES collapse pressure. Honest caveat: uniform-mutation
  overshoots the RNN's forward-KL, evidence its smoothing prior is
  truth-like, not uniform (future refinement).

This mechanistically explains the architecture-generality result and the
softened neural g*, and develops the estimator axis Riis names as future
work. New: knowledge/kernel.py, configs/layer1/kernel_{sharpen,smooth}.yaml,
figures/plot_kernel.py (overlays analytic arms vs committed neural
endpoints), READMEs, tests/test_kernel.py (+6, 105 total green). Strategic
Riis positioning recorded in CLAUDE.md: concede "collapse=drift" as prior
art; lead with recombination, the kernel axis, and the Lamarckian society.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tasks/todo.md

@@ -245,8 +245,33 @@ C3 vertical claim deferred.*
   confirmed on real images. VAE needs ~10% grounding vs synthetic ~5% (cf. the `grounding` finding).
   **99 tests green** (+5 torchvision-gated). `make mnist` / `make env-mnist` (kept out of `make neural`).
 
+**2026-07-05 — learning kernel (Layer-1 extension) + Riis positioning.**
+
+- Prompted by revisiting Layer 1 vs 1.5 and the Riis 2026 paper (arXiv:2604.08554). Added
+  `knowledge/kernel.py` (`LearningKernelCfg`: reset `u` = smoothing, temperature `τ` = sharpening,
+  floor `ε`), wired into `step.generation_step` (post-refit) / `StepCtx` / `DynamicsCfg` — **identity
+  by default, so the 68 Layer-1 scientific-validation + correctness tests are unchanged.**
+- **Result:** neutral Wright–Fisher fails BOTH neural models, oppositely. VAE regime (n=6000,K=30):
+  neutral is inert, sharpening `τ=0.8` reproduces the collapse-to-one-mode. RNN regime (n=200,K=256):
+  neutral → H=0, mutation `u=0.006` reproduces the H-floor (~0.68). Uniform-mutation overshoots the
+  RNN's KL → its prior is truth-like, not uniform (honest caveat, future refinement).
+- `configs/layer1/kernel_{sharpen,smooth}.yaml`, `figures/plot_kernel.py` (overlays analytic arms vs
+  the committed neural endpoints), READMEs, `tests/test_kernel.py` (+6). Wired into `make layer1`.
+- **Strategic (see CLAUDE.md finding):** concede "collapse=drift" to Riis (prior art; cite); his
+  mixed environment retains OLD SYNTHETIC (no real-data injection) → pessimistic, no g* that prevents
+  collapse. Our defensible novelty: recombination "merge-don't-average" (flagship), the learning-kernel
+  axis (he flags as future work), grounding-as-immigration, real-weights+MNIST breadth, and the
+  Lamarckian society + vertical/cumulative C3 claim (not yet run). Reposition: from "collapse is drift"
+  to a population-genetic CONTROL THEORY for sustaining open-ended knowledge.
+
 ## Remaining (all optional)
 
+- [ ] **Learning-kernel refinement:** truth-like smoothing prior (`prior="truth"`) + measurement floor
+  for a quantitative RNN match; **multi-locus / linkage** modes (class×style) as the rigorous home for
+  recombination. Both enrich predictive power and separate us further from Riis's single-locus n-grams.
+- [ ] **The Lamarckian society experiments** (multi-agent grounding + decorrelated specialists +
+  recombination + QD-selection + re-mint) and the **vertical/cumulative C3 claim** — the highest-ceiling,
+  wholly-novel frame; not yet entered.
 - [ ] **`region_matched`** grounding (R>1), **`remint`** re-mint gate (optional).
 - [ ] **VAE fidelity:** fix the prior-hole mismatch (KL-annealing / free-bits / larger latent) so it
   clears the gen-0 gate, then add to `architectures`. Or document as a known limitation.